ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine最新文献

Early amyloid-β deposition is a hallmark of Alzheimer's disease (AD), though the exact nature of amyloid pathogenesis is not fully characterized. In this study, we designed a network diffusion model to simulate the spread of amyloid pathology through white matter brain networks of diagnostic subpopulations of healthy control (HC), mild cognitive impairment (MCI), and AD. Our network diffusion model was able to successfully model the spread of amyloid, recapturing regional distributions of amyloid observed in ¹⁸F-florbetapir positron emission tomography (r=0.44-0.46, P<0.01). When tuning the optimal parameters, we found that the optimal diffusion time (t) provided a notion of temporal progression, where the HC group had the lowest time (t = 107.22 ± 16.67), followed by MCI (t = 122.78 ± 19.63), and lastly AD (t =136.20 ± 24.47). The optimal starting seeds were the brainstem in all three diagnostic groups, followed by the lateral orbitofrontal lobes for HC and MCI and the lingual gyri in AD. Our findings corroborate evidence from amyloid staging studies where amyloid starts in the primary neocortex and associative cortex. The significance of the white matter structural network in the diffusion process provides evidence for the trans-synaptic spread hypothesis of amyloid in AD. In conclusion, our study provides novel insights into the pathogenesis of amyloid in AD and its subsequent propagation throughout the brain.

Early and accurate diagnosis of Alzheimer's disease (AD), a complex neurodegenerative disorder, requires analysis of heterogeneous biomarkers (e.g., neuroimaging, genetic risk factors, cognitive tests, and cerebrospinal fluid proteins) typically represented in a tabular format. With flexible few-shot reasoning, multimodal integration, and natural-language-based interpretability, large language models (LLMs) offer unprecedented opportunities for prediction with structured biomedical data. We propose a novel framework called TAP-GPT, T abular A lzheimer's P rediction GPT, that adapts TableGPT2, a multimodal tabular-specialized LLM originally developed for business intelligence tasks, for AD diagnosis using structured biomarker data with small sample sizes. Our approach constructs few-shot tabular prompts using in-context learning examples from structured biomedical data and finetunes TableGPT2 using the parameter-efficient qLoRA adaption for a clinical binary classification task of AD or cognitively normal (CN). The TAP-GPT framework harnesses the powerful tabular understanding ability of TableGPT2 and the encoded prior knowledge of LLMs to outperform more advanced general-purpose LLMs and a tabular foundation model (TFM) developed for prediction tasks. To our knowledge, this is the first application of LLMs to the prediction task using tabular biomarker data, paving the way for future LLM-driven multi-agent frameworks in biomedical informatics.

Canonical correlation analysis (CCA) is a technique for finding correlations between different data modalities and learning low-dimensional representations. As fairness becomes crucial in machine learning, fair CCA has gained attention. However, previous approaches often overlook the impact on downstream classification tasks, limiting applicability. We propose a novel fair CCA method for fair representation learning, ensuring the projected features are independent of sensitive attributes, thus enhancing fairness without compromising accuracy. We validate our method on synthetic data and real-world data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), demonstrating its ability to maintain high correlation analysis performance while improving fairness in classification tasks. Our work enables fair machine learning in neuroimaging studies where unbiased analysis is essential. Code is available in https://github.com/ZhanliangAaronWang/FR-CCA-ADNI.

Recently, there has been a revived interest in system neuroscience causation models due to their unique capability to unravel complex relationships in multi-scale brain networks. In this paper, our goal is to verify the feasibility and effectiveness of using a causality-based approach for fMRI fingerprinting. Specifically, we propose an innovative method that utilizes the causal dynamics activities of the brain to identify the unique cognitive patterns of individuals (e.g., subject fingerprint) and fMRI tasks (e.g., task fingerprint). The key novelty of our approach stems from the development of a two-timescale linear state-space model to extract 'spatio-temporal' (aka causal) signatures from an individual's fMRI time series data. To the best of our knowledge, we pioneer and subsequently quantify, in this paper, the concept of 'causal fingerprint.' Our method is well-separated from other fingerprint studies as we quantify fingerprints from a cause-and-effect perspective, which are then incorporated with a modal decomposition and projection method to perform subject identification and a GNN-based (Graph Neural Network) model to perform task identification. Finally, we show that the experimental results and comparisons with non-causality-based methods demonstrate the effectiveness of the proposed methods. We visualize the obtained causal signatures and discuss their biological relevance in light of the existing understanding of brain functionalities. Collectively, our work paves the way for further studies on causal fingerprints with potential applications in both healthy controls and neurodegenerative diseases.

Imaging technologies have revolutionized the study of the tumor microenvironment (TME) by leveraging spatial analysis, which enables the exploration of tissue organization and cellular communication, as well as aiding cancer diagnosis and prognosis. However, while many advanced spatial analysis methods have been recently published, they are enmeshed with specific imaging technology. An opportunity exists to develop a technology-agnostic methodology that captures complex spatial patterns in the TME as phenotypes to use in downstream tasks. In this paper, we present a novel variation of spatial g-function and a comprehensive imaging-technology-agnostic framework that identifies rich spatial phenotypes that can be used in survival analysis and classification tasks. Applying our methodology to breast cancer, we uncover spatial phenotypes with significance to survival across racial groups and molecular subtypes of breast cancer. We find other phenotypes that are significant to the survival of specific patient categories (such as African American). We also demonstrate that our phenotypes reflect specific biological contexts. These results highlight the relevance of our proposed spatial analysis and phenotype discovery pipeline and demonstrate the benefits of the systematic exploration of spatial phenotypes for more personalized diagnosis and treatments.

Image registration is important in biological image analysis; however, it is often challenged by distortions and non-linear transformations. In this paper, we present a novel patch-wise image registration method to address the mentioned issues. Our method begins with global registration to correct linear transformations, followed by a detailed examination of geometrical distortions. After that, each image is adaptively divided into patches to isolate and correct non-linear distortions, followed by reconstruction and combining patches using Otsu thresholding. We evaluated our method against state-of-the-art techniques using mutual information (MI), phase congruency-based (PCB), and gradient-based metrics (GBM) across four real biology datasets. Our results demonstrate superior feature alignment and image coherence, especially in serial-stack registrations. While the proposed method has longer processing times compared to linear registration methods, its enhanced accuracy and reliability to handle non-uniform distortion makes it beneficial for precision-demanding applications. We have created a public GitHub repository containing the code used in our research, available at https://github.com/NabaviLab/CAPTURE.

Tensor Canonical Correlation Analysis (TCCA) is a commonly employed statistical method utilized to examine linear associations between two sets of tensor datasets. However, the existing TCCA models fail to adequately address the heterogeneity present in real-world tensor data, such as brain imaging data collected from diverse groups characterized by factors like sex and race. Consequently, these models may yield biased outcomes. In order to surmount this constraint, we propose a novel approach called Multi-Group TCCA (MG-TCCA), which enables the joint analysis of multiple subgroups. By incorporating a dual sparsity structure and a block coordinate ascent algorithm, our MG-TCCA method effectively addresses heterogeneity and leverages information across different groups to identify consistent signals. This novel approach facilitates the quantification of shared and individual structures, reduces data dimensionality, and enables visual exploration. To empirically validate our approach, we conduct a study focused on investigating correlations between two brain positron emission tomography (PET) modalities (AV-45 and FDG) within an Alzheimer's disease (AD) cohort. Our results demonstrate that MG-TCCA surpasses traditional TCCA in identifying sex-specific cross-modality imaging correlations. This heightened performance of MG-TCCA provides valuable insights for the characterization of multimodal imaging biomarkers in AD.

Clinical EHR data is naturally heterogeneous, where it contains abundant sub-phenotype. Such diversity creates challenges for outcome prediction using a machine learning model since it leads to high intra-class variance. To address this issue, we propose a supervised pre-training model with a unique embedded k-nearest-neighbor positive sampling strategy. We demonstrate the enhanced performance value of this framework theoretically and show that it yields highly competitive experimental results in predicting patient mortality in real-world COVID-19 EHR data with a total of over 7,000 patients admitted to a large, urban health system. Our method achieves a better AUROC prediction score of 0.872, which outperforms the alternative pre-training models and traditional machine learning methods. Additionally, our method performs much better when the training data size is small (345 training instances).

Automatic segmentation of thoracic cavity structures in computer tomography (CT) is a key step for applications ranging from radiotherapy planning to imaging biomarker discovery with radiomics approaches. State-of-the-art segmentation can be provided by fully convolutional neural networks such as the U-Net or V-Net. However, there is a very limited body of work on a comparative analysis of the performance of these architectures for chest CTs with significant neoplastic disease. In this work, we compared four different types of fully convolutional architectures using the same pre-processing and post-processing pipelines. These methods were evaluated using a dataset of CT images and thoracic cavity segmentations from 402 cancer patients. We found that these methods achieved very high segmentation performance by benchmarks of three evaluation criteria, i.e. Dice coefficient, average symmetric surface distance and 95% Hausdorff distance. Overall, the two-stage 3D U-Net model performed slightly better than other models, with Dice coefficients for left and right lung reaching 0.947 and 0.952, respectively. However, 3D U-Net model achieved the best performance under the evaluation of HD95 for right lung and ASSD for both left and right lung. These results demonstrate that the current state-of-art deep learning models can work very well for segmenting not only healthy lungs but also the lung containing different stages of cancerous lesions. The comprehensive types of lung masks from these evaluated methods enabled the creation of imaging-based biomarkers representing both healthy lung parenchyma and neoplastic lesions, allowing us to utilize these segmented areas for the downstream analysis, e.g. treatment planning, prognosis and survival prediction.

Tracking population-level cancer information is essential for researchers, clinicians, policymakers, and the public. Unfortunately, much of the information is stored as unstructured data in pathology reports. Thus, too process the information, we require either automated extraction techniques or manual curation. Moreover, many of the cancer-related concepts appear infrequently in real-world training datasets. Automated extraction is difficult because of the limited data. This study introduces a novel technique that incorporates structured expert knowledge to improve histology and topography code classification models. Using pathology reports collected from the Kentucky Cancer Registry, we introduce a novel multi-task training approach with hierarchical regularization that incorporates structured information about the International Classification of Diseases for Oncology, 3rd Edition classes to improve predictive performance. Overall, we find that our method improves both micro and macro F1. For macro F1, we achieve up to a 6% absolute improvement for topography codes and up to 4% absolute improvement for histology codes.